Dataset Information

Reliability of Scales With Second-Order Structure: Evaluation of Coefficient Alpha's Population Slippage Using Latent Variable Modeling.

ABSTRACT: A readily applicable procedure is discussed that allows evaluation of the discrepancy between the popular coefficient alpha and the reliability coefficient of a scale with second-order factorial structure that is frequently of relevance in empirical educational and psychological research. The approach is developed within the framework of the widely used latent variable modeling methodology and permits point and interval estimation of the slippage of alpha from scale reliability in a population under investigation. The method is useful when examining the consistency of complex structure measuring instruments assessing higher order latent constructs and, under its assumptions, represents a generally recommendable alternative to coefficient alpha. The outlined procedure is illustrated using data from an authoritarianism study.

SUBMITTER: Raykov T

PROVIDER: S-EPMC6293413 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Reliability of Scales With Second-Order Structure: Evaluation of Coefficient Alpha's Population Slippage Using Latent Variable Modeling.

Raykov Tenko T Goldammer Philippe P Marcoulides George A GA Li Tatyana T Menold Natalja N

Educational and psychological measurement 20170724 6

A readily applicable procedure is discussed that allows evaluation of the discrepancy between the popular coefficient alpha and the reliability coefficient of a scale with second-order factorial structure that is frequently of relevance in empirical educational and psychological research. The approach is developed within the framework of the widely used latent variable modeling methodology and permits point and interval estimation of the slippage of alpha from scale reliability in a population u ...[more]

PMID: 30559517

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Project description:Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

Dataset Information

Reliability of Scales With Second-Order Structure: Evaluation of Coefficient Alpha's Population Slippage Using Latent Variable Modeling.

Publications

Reliability of Scales With Second-Order Structure: Evaluation of Coefficient Alpha's Population Slippage Using Latent Variable Modeling.

Similar Datasets

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